Tidy data frames (Atlantic Ocean Stocks, 1930-2018):

Total Biomass 1930-2018 (Atlantic Ocean Stocks)

#Tidy format: tb.data --- Total biomass data
#No data: ATBTUNAEATL, ATBTUNAWATL

tuna_biomass <- tb.data %>% 
  select(ALBANATL, ALBASATL, BIGEYEATL, BMARLINATL, SAILEATL, SAILWATL, SKJEATL, SKJWATL, SWORDNATL, SWORDSATL, WMARLINATL, YFINATL)%>% 
  rownames_to_column() %>% 
  pivot_longer(!rowname, names_to = "species", values_to = "biomass") %>% 
  rename(year = rowname) %>% 
  mutate(year = as.numeric(as.character(year))) %>% 
  filter(year >= 1930)

Total Catch 1930-2018 (Atlantic Ocean Stocks)

#Tidy format: tc.data --- Total catch data
#Data for all 14 species
tuna_catch <- tc.data %>% 
  select(ALBANATL, ALBASATL, BIGEYEATL, BMARLINATL, SAILEATL, SAILWATL, SKJEATL, SKJWATL, SWORDNATL, SWORDSATL, WMARLINATL, YFINATL, ATBTUNAEATL, ATBTUNAWATL)%>% 
  rownames_to_column() %>% 
  pivot_longer(!rowname, names_to = "species", values_to = "catch") %>% 
  rename(year = rowname) %>%
  mutate(year = as.numeric(as.character(year))) %>%
  filter(year >= 1930)

Explotation Rate 1930-2018 (Atlantic Ocean Stocks)

#Tidy format: er.data --- Exploitation rate data (usually an annual fraction harvested)
#No data: ATBTUNAEATL, ATBTUNAWATL

#NOTE: harvest rate (U); may be either exploitation rate or fishing mortality

tuna_er <- er.data %>% 
  select(ALBANATL, ALBASATL, BIGEYEATL, BMARLINATL, SAILEATL, SAILWATL, SKJEATL, SKJWATL, SWORDNATL, SWORDSATL, WMARLINATL, YFINATL) %>% 
  rownames_to_column() %>% 
  pivot_longer(!rowname, names_to = "species", values_to = "explotation_rate") %>% 
  rename(year = rowname) %>%
  mutate(year = as.numeric(as.character(year))) %>%
  filter(year >= 1930)

Fishing Mortality 1930-2018 (Atlantic Ocean Stocks)

(Probably we’ll use Explotation Rate instead of Fishing Mortality)

#Tidy format: f.data --- Fishing mortality data (usually an instantaneous rate)
#No data: SAILEATL, SAILWATL, WMARLINATL

tuna_f <- f.data %>% 
  select(ALBANATL, ALBASATL, BIGEYEATL, BMARLINATL, SKJEATL, SKJWATL, SWORDNATL, SWORDSATL, YFINATL, ATBTUNAEATL, ATBTUNAWATL) %>% 
  rownames_to_column() %>% 
  pivot_longer(!rowname, names_to = "species", values_to = "explotation_rate") %>% 
  rename(year = rowname) %>%
  mutate(year = as.numeric(as.character(year))) %>%
  filter(year >= 1930)

B/Bmsy 1930-2018 (Atlantic Ocean Stocks)

#Tidy format: divbpref.data --- B/Bmsy pref data (B/Bmsy if available, otherwise B/Bmgt)
#Data for all 14 species

tuna_b_bmsy <- divbpref.data %>% 
  select(ALBANATL, ALBASATL, BIGEYEATL, BMARLINATL, SAILEATL, SAILWATL, SKJEATL, SKJWATL, SWORDNATL, SWORDSATL, WMARLINATL, YFINATL, ATBTUNAEATL, ATBTUNAWATL) %>% 
  rownames_to_column() %>% 
  pivot_longer(!rowname, names_to = "species", values_to = "b_bmsy") %>% 
  rename(year = rowname) %>%
  mutate(year = as.numeric(as.character(year))) %>%
  filter(year >= 1930)

U/Umsy 1930-2018 (Atlantic Ocean Stocks)

#Tidy format: divupref.data --- U/Umsy pref data (U/Umsy if available, otherwise U/Umgt)
#Data for all 14 species

tuna_u_umsy <- divupref.data %>% 
  select(ALBANATL, ALBASATL, BIGEYEATL, BMARLINATL, SAILEATL, SAILWATL, SKJEATL, SKJWATL, SWORDNATL, SWORDSATL, WMARLINATL, YFINATL, ATBTUNAEATL, ATBTUNAWATL) %>% 
  rownames_to_column() %>% 
  pivot_longer(!rowname, names_to = "species", values_to = "u_umsy") %>% 
  rename(year = rowname) %>%
  mutate(year = as.numeric(as.character(year))) %>%
  filter(year >= 1930)

Some plots to explore values

#Biomass plot:
biomass_plot <- ggplot(data = tuna_biomass, aes(x = year, y = biomass)) +
geom_line(aes(color = species))

#Total Catch plot: 
catch_plot <- ggplot(data = tuna_catch, aes(x = year, y = catch)) +
geom_line(aes(color = species))

#Explotation Rate plot:
er_plot <- ggplot(data = tuna_er, aes(x = year, y = explotation_rate)) +
geom_line(aes(color = species)) 

#B/Bmsy plot: tuna_b_bmsy
b_bmsy_plot <- ggplot(data = tuna_b_bmsy, aes(x = year, y = b_bmsy)) +
geom_line(aes(color = species)) 


#U/Umsy plot: 
u_umsy_plot <- ggplot(data = tuna_u_umsy, aes(x = year, y = u_umsy)) +
geom_line(aes(color = species)) 
#Interactive exploratory plots:

ggplotly(biomass_plot)
ggplotly(catch_plot)
ggplotly(er_plot)
ggplotly(b_bmsy_plot)
ggplotly(u_umsy_plot)
#can highlight species, values, or specific points with `gghighlight`